Computers can be utilized to perform processing of graphic images in a digital format, such as, for example, photographs, still images from videos, and so on. Often, the goal of such processing is to locate objects of interest (e.g., faces or other areas/features) in a particular image. A computer is capable of detecting most or all well-defined instances of the object in the image, if a sufficient amount of processing time to process the image can be afforded. One common goal for object detection is the detection of human faces. Detecting objects is also useful for user interfaces, scanning of image databases, teleconferencing, electronic processing of photographs, and other suitable applications. The appearance of the objects varies greatly across individuals, images, camera locations, and illuminations.
The detection and precise localization of objects in images is a time demanding task, particularly when prior information is unavailable regarding their possible size and location. In such cases, it can be assumed that the sought objects may possess an arbitrary size and may be placed at arbitrary locations within the image. A number of prior art methods for the detection and localization of objects, such as faces in images, have been implemented. Such methods typically involve searching images for objects utilizing a scanning approach. The windows of various sizes are systematically placed at all possible locations within the image and evaluated by an object detector that determines whether an object is presented in a current scanning window.
A problem associated with such scanning approaches is that the number of possible windows in which the object is sought is extremely high even for relatively small small images, which results in a lengthy processing time. The majority of prior art techniques involve employing scanning windows of certain scales and shifting such windows utilizing a larger step whose size may be derived from the actual size of the scanning window. This strategy decreases the number of inspected windows, which generally speeds up the detection and localization process, but negatively affects detection performance and localization accuracy. Further, many detections may be lost due to skipped regions and the objects cannot be framed properly.
Therefore, a need exists for an improved method and system for object detection and localization in images. Additionally, a need exists for providing a methodology, for adaptive scanning of images to speed up the detection and localization process as disclosed in further detail herein.